function net = vggFCN_fc678_BNmLoss_init()
net = vggFCN_fc678_BNmLoss();
opts.sourceModelPath='../data/models/imagenet-vgg-verydeep-16.mat';
netVgg = vl_simplenn_tidy(load(opts.sourceModelPath)) ;
netVgg = dagnn.DagNN.fromSimpleNN(netVgg, 'canonicalNames', true) ;


% %%
% Nb=2:2:32;Nk=1:2:31;
% Nvb=2:2:32;Nvk=1:2:31;
% for i=1:length(Nb)-1
%     net.params(Nb(i)).value=netVgg.params(Nvb(i)).value;
%     net.params(Nk(i)).value=netVgg.params(Nvk(i)).value;
%     net.params(Nb(i)).learningRate = 2 ;
% %     net.params(Nk(i)).learningRate = 0 ;
% end
% net.params(31).value=zeros(size(net.params(31).value),'single');
% net.params(32).value=zeros(size(net.params(32).value),'single');
% 
% net.params(33).value=single(rand(size(net.params(33).value)));

% %%
% Nb=5:5:75;Nk=4:5:74;
% Nvb=2:2:30;Nvk=1:2:29;
% for i=1:length(Nb)
%     net.params(Nb(i)).value=netVgg.params(Nvb(i)).value;
%     net.params(Nk(i)).value=netVgg.params(Nvk(i)).value;
%     net.params(Nb(i)).learningRate = 2 ;
% %     net.params(Nk(i)).learningRate = 0 ;
% end
% net.params(76).value=zeros(size(net.params(76).value),'single');
% net.params(77).value=zeros(size(net.params(77).value),'single');
% 
% net.params(78).value=single(rand(size(net.params(78).value)));